Performance analysis of directional CSMA/CA in the presence of deafness
Bibliographic record
Abstract
Although directional antennas can increase the spatial reuse in wireless ad hoc networks, the directional carrier sense multiple access/collision avoidance (CSMA/CA) protocols encounter unprecedented challenges that can offset this potential benefit. One critical problem is known as deafness that occurs when a transmitter repeatedly fails to communicate with its intended receiver because the receiver is beamformed towards another direction. The deafness problem has not yet been analytically studied since existing analytical models for directional CSMA/CA ignore the effect of deafness. In this study, the authors develop an analytical framework for directional CSMA/CA, which is the first analytical model to consider the problem of deafness as a source of transmission failures in multi-hop wireless networks with directional antennas. They also propose a deafness index to quantify the negative impact of deafness. Using their framework, the authors study the tradeoff between spatial reuse and deafness when a directional CSMA/CA protocol is employed. Their results demonstrate that decreasing the antenna beamwidth increases the saturation throughput up to a certain limit corresponding to an optimum beamwidth. However, by further lowering the beamwidth, the negative impact of deafness offsets the benefits of spatial reuse and results in a steep decrease in the saturation throughput. These results prove analytically that deafness is a critical problem if left unaddressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".